• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.69-76
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• 2012

This paper proposes a novel way of personalized web search through re-ranking the search results with user profiles of concept-network structure. Basically, personalized search systems need to be based on user profiles that contain users' search patterns, and they actively use the user profiles in order to expand initial queries or to re-rank the search results. The proposed method is a sort of a re-ranking personalized search method integrated with query expansion facility. The method identifies some documents which occur commonly among a set of different search results from the expanded queries, and re-ranks the search results by the degree of co-occurring. We show that the proposed method outperforms the conventional ones by performing the empirical web search with a number of actual users who have diverse information needs and query intents.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.503-506
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• 2006

The amounts of information and services are rapidly increasing. This causes inefficiency of retrievals of information overload problem. To overcome this kind of problems, This article suggests Odin system, keyword suggestion system based on Thesaurus. Odin system will suggest appropriate search-words and this leads to the user's satisfied result.

• Science of Emotion and Sensibility
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• v.22 no.2
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• pp.49-58
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• 2019

In the era of the 4th Industrial Revolution, artificial intelligence (AI) has become one of the core technologies in terms of the business strategy among information technology companies. Both international and domestic major portal companies are launching AI search services. These AI search services utilize voice, images, and other unstructured data to provide different experiences from existing text-based search services. An unfamiliar experience is a factor that can hinder the usability of the service. Therefore, the usability testing of the AI search services is necessary. This study examines the usability of the AI search service on the Naver App 8.9.3 beta version by comparing it with the search services of the current Naver App and targets 30 people in their 20s and 30s, who have experience using Naver apps. The usability of Smart Lens, Smart Voice, Smart Around, and AiRS, which are the Naver App beta versions of their artificial intelligence search service, is evaluated and statistically significant usability changes are revealed. Smart Lens, Smart Voice, and Smart Around exhibited positive changes, whereas AiRS exhibited negative changes in terms of usability. This study evaluates the change in usability according to the application of the artificial intelligence search services and investigates the correlation between the evaluation factors. The obtained data are expected to be useful for the usability evaluation of services that use AI.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.206-207
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• 2018

방대한 정보를 사용자에게 제공하기 위해 검색 엔진은 다양한 알고리즘을 통해 사용자마다의 최적화된 정보를 구성한다. 과제, 논문, 특허, 연구보고서 등 과학기술정보를 서비스 하는 주체 역시 나름의 검색 알고리즘으로 정보를 제공하지만, 질의어와 문서간의 적합도만을 측정하여 검색 결과를 제시할 뿐 사용자의 관심 분야나 요구를 반영하지 않고 있다. 특히 관심 분야에 적합한 과학기술정보를 사용자가 접근하기 쉽게 제공하는 것은 매우 중요하다. 본 논문에서는 사용자 관심분야를 서비스 이용행태로부터 결정하여 이를 과학기술정보 개인화에 반영하는 서비스에 대해 제안하였다. 이를 위해 실시간 관심분야 추적, 관심 태그 클라우드 제공, 관심분야 기반 추천정보 제공, 검색 결과 개인화 네 가지 기능으로 구성된 과학기술정보 개인화 서비스를 설계하고 구현하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.105-108
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• 2010

쌍방향으로 질문과 답변을 하는 커뮤니티 기반의 지식검색서비스에서는 질의를 통해 원하는 답변을 얻을 수 있지만, 수많은 사용자들이 참여함에 따라 방대한 문서 속에서 검증된 문서를 찾아내는 것은 점점 더 어려워지고 있다. 지식검색서비스에서 기존 연구는 사용자들이 생성한 데이터 즉 추천수, 조회수 등의 비텍스트 정보를 이용하거나 답변의 길이, 자료첨부, 연결어 등의 텍스트 정보 이용하여 전문가를 식별하거나 문서의 품질을 평가하고, 이를 검색에 반영하여 검색성능을 향상시키는 데 활용했다. 그러나 비텍스트 정보는 질의/응답의 초기에 사용자들에 의해 충분한 정보를 확보할 수 없는 단점이 제기 되며, 텍스트 정보는 전체의 문서를 답변의 길이, 자료 첨부등과 같은 일부요인으로 판단해야하기 때문에 품질평가의 한계가 있다고 볼 수 있겠다. 본 논문에서는 이러한 비텍스트 정보와 텍스트 정보의 문제점을 개선하기 위한 품질평가 알고리즘을 제안한다. 제안된 알고리즘을 통한 품질지수는 텍스트/비텍스트 정보와 소셜 네트워크 사용자 중앙성을 고려하여 질문에 적합하고 신뢰성 있는 답변을 랭킹화 함으로써 지식검색문서를 분별하는 지표가 되며, 이는 지식검색서비스의 성능향상에 기여를 할 수 있을 것으로 기대된다.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.

• The Journal of Society for e-Business Studies
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• v.15 no.4
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• pp.181-195
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• 2010

The participation and share between personal users are the driving force of Web 2.0, and easily found in blog, social network, collective intelligence, social bookmarking and tagging. Among those applications, the social bookmarking lets Internet users to store bookmarks online and share them, and provides various services based on shared bookmarks which people think important.Delicious.com is the representative site of social bookmarking services, and provides a bookmark search service by using tags which users attach to the bookmarks. Our paper suggests a method re-ranking the ranks from Delicious.com based on user tags in order to provide personalized bookmark recommendations. Moreover, a method to consider bookmarks which have tags not directly related to the user query keywords is suggested by using tag network based on Jaccard similarity coefficient. The performance of suggested system is verified with experiments that compare the ranks by Delicious.com with new ranks of our system.

• The Journal of Society for e-Business Studies
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• v.10 no.2
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• pp.109-132
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• 2005

With the flood of multimedia contents over the digital TV channels, the internet, and etc., users sometimes have a difficulty in finding their preferred contents, spend heavy surfing time to find them, and are even very likely to miss them while searching. In this paper we suggests two-step clustering technique using time schema on how the system can recommend the user's preferred contents based on the collaborative filtering that has been proved to be successful when new users appeared. This method maps and recommends users' profile according to the gender and age at the first step, and then recommends a probabilistic item clustering customers who choose the same item at the same time based on time schema at the second stage. In addition, this has improved the accuracy of predictions in recommendation and the efficiency in time calculation by reflecting feedbacks of the result of the recommender engine and dynamically update customers' preference.

• The Journal of Society for e-Business Studies
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• v.21 no.3
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• pp.15-28
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• 2016

This paper proposes a system recommending spatial information what user wants with collecting and analyzing tweets around the user's location by using the GPS information acquired in mobile. This system has built an emotion dictionary and then derive the recommendation score of morphological analyzed tweets to provide not just simple information but recommendation through the emotion analysis information. The system also calculates distance between the recommended tweets and user's latitude-longitude coordinates and the results showed the close order. This paper evaluates the result of the emotion analysis in a total of 10 areas with two keyword 'Restaurants' and 'Performance.' In the result, the number of tweets containing the words positive or negative are 122 of the total 210. In addition, 65 tweets classified as positive or negative by analyzing emotions after a morphological analysis and only 46 tweets contained the meaning of the positive or negative actually. This result shows the system detected tweets containing the emotional element with recall of 38% and performed emotion analysis with precision of 71%.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.317-342
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• 2024

The purpose of this study is to explore ways to improve accessibility to school library materials through analysis of elementary school students' information search behavior in DLS. Accordingly, the DLS search process was recorded for 26 students attempting a DLS search in the school library, and data was collected through a pre-search questionnaire on overall information needs and a post-search questionnaire on the search process and results. As a result of the analysis, satisfaction was found to be low when the main purpose of DLS use was simple leisure reading, when the search time and number of search words were long, and when there were too many search results. Accordingly, it was emphasized that curriculum subject-related metadata elements should be developed and a curriculum subject-specific thesaurus should be built and used to build lists and support user searches. In addition, it was suggested that the basic functions provided in external searches should be included, and a foundation should be laid in terms of resources and curriculum to systematically provide information utilization education to elementary school students who lack the ability to select search terms and judge the suitability of results after the search. It was proposed to provide an integrated search service with external resources and a personalized book recommendation service.